Despite being studied extensively for a few decades, handwritten character
recognition (HCR) is still considered a challenging learning problem in pattern
recognition, and there is very limited research on script independent models.
This is mainly because of similarity in structure of characters, different
handwriting styles, noisy datasets, diversity of scripts, focus of the
conventional research on handcrafted feature extraction techniques, and
unavailability of public datasets and code-repositories to reproduce the
results. On the other hand, deep learning has witnessed huge success in
different areas of pattern recognition, including HCR, and provides an
end-to-end learning. However, deep learning techniques are computationally
expensive, need large amount of data for training and have been developed for
specific scripts only. To address the above limitations, we have proposed a
novel generic deep learning architecture for script independent handwritten
character recognition, called HCR-Net. HCR-Net is based on a novel transfer
learning approach for HCR, which partly utilizes feature extraction layers of a
pre-trained network. Due to transfer learning and image-augmentation, HCR-Net
provides faster and computationally efficient training, better performance and
better generalizations, and can work with small datasets. HCR-Net is
extensively evaluated on 40 publicly available datasets of Bangla, Punjabi,
Hindi, English, Swedish, Urdu, Farsi, Tibetan, Kannada, Malayalam, Telugu,
Marathi, Nepali and Arabic languages, and established 26 new benchmark results
while performed close to the best results in the rest cases. HCR-Net showed
performance improvements up to 11% against the existing results and achieved a
fast convergence rate showing up to 99% of final performance in the very first
epoch. HCR-Net significantly outperformed the state-of-the-art transfer
learning techniques...